Process optimisation

Every process that happens in a company or industry should be evaluated on a regular basis to ensure each component is working to its maximum potential. The easiest way to monitor complex modern business processes is through Artificial Intelligence. One example is the use of the Internet of Things (IoT) to constantly track physical components in processes that need optimization. The key to process optimisation is processing data to define optimization opportunities. The available machine learning methods are able to pick up patterns that are invisible to a human observer, even rivalling an expert’s hunch. Any business can benefit from increased efficiency in three areas: equipment optimisation, control optimisation and operating procedures. AI and ML find specific structures within these areas and provide actionable insights that will optimise the processes. The method of process optimisation is one of the most fundamental tools in business to ensure the complete business ecosystem works to its full potential.

Transportation routing

Transportation routing is the name of an optimization problem in the field of logistics.A company has many factors to consider when setting efficient routes. These aspects can be constraints such as vehicle capacity, road access issues, and timing of delivery and pick up. Each part of the logistics process needs to be working optimally in order for companies to ensure they keep service agreements and maximize revenues. This means that the traffic and route options need to be understood and optimised as fast as possible so that every piece of the journey can run smoothly. As this kind of industry has many factors to consider, the inclusion of Artificial Intelligence has become a valid and essential part of the entire process. In the transport sector AI optimises vehicle capacity, ensures the best routes are chosen, and maximises the turnover of the logistics company, leading to higher revenues. The benefits of AI in the transport sector not only support the company, they can also impact the environment and local economy.

Natural Language Processing

Natural Language Processing (NLP) is an area of linguistics and computer science where an algorithm is trained to infer structure from natural human language. Ordinarily, a computer wouldn’t understand vague language patterns, however, Natural Language Processing engines are designed to extract meaning from naturally ambiguous human language patterns. This allows the algorithms to be used for cases such as chatbots, sentiment analysis, and tagging parts of speech. Highly popular examples of NLP are the Amazon Echo and Google Home, but this branch of artificial intelligence is widely applicable in the industry. A NLP engine embedded in a chat-bot would be valuable for sales businesses (and even banking), while sentiment analysis is already used in the world of social media. This form of artificial intelligence is relatively new, so more developments are sure to come in the future.

Social Listening

Brand awareness is an important aspect of a company. Without a solid name or reputation a company could become obsolete, especially in the modern world of digitised business. Content such as reviews, feedback, and social media mentions need to be closely followed to ensure the company maintains a good reputation, which is facilitated by Social Listening. By paying close attention to the social data stream a company can utilize Machine Learning and analytics to control its public profile. Sentiment analysis, social network analysis and industry trends analysis can create actionable responses for a company to undertake, and these are all part of Social Listening. The insights this method brings can drive business growth in the highly dynamic social environment of tomorrow.

Insurance Claims

The insurance industry collects and stores vast amounts of data. This data can be used to build Machine Learning models for fast and accurate analysis of insurance claims. This is a rapidly growing segment of artificial intelligence and is expected to dominate the insurance sector in the future. Some insurance claims can be carried out entirely by Artificial Intelligence. For example, a chatbot (using an NLP model) backed by an insurance claims ML model could solve claims without the need for human intervention. Such a system can work 24/7 and provide fast and effective solutions to customers across the world in seconds. Bringing Artificial Intelligence and Machine Learning into the insurance industry will also help detect possible fraudulent behaviours and point to actionable measures, and it has already started to revolutionise the market.

Fraud Detection

Fraud detection and prevention is vital for any financial institution. Without stringent measures in place, the business becomes unprofitable. Artificial Intelligence is one of the best ways to implement effective fraud detection. Using AI systems to detect fraud can ensure fast responses and accurate analysis, which decreases the impact of fraudulent behaviours. In most cases a company would utilise Machine Learning techniques to define patterns of fraud. ML can detect fraudulent behaviours that no human could, and it can be applied to large data sets. The engine can also use future data to improve accuracy and adjust to new types of fraudulent activity. Without data science techniques geared towards fraud detection a financial institution is more likely to miss the signs, leading to expensive mistakes.


Nowadays, doctors and surgeons have lots of patient data to sift through and make decisions on. This is especially true for cardiologists who collect more data from patients compared to other areas of medicine. This biomedical data is constantly increasing, which makes it harder and more time consuming for cardiologists to develop a clear picture of what needs to be done. One way to tackle this abundance of data is with Artificial Intelligence. Researchers push the development of Machine Learning models for diagnostics and cardiology. These technologies allow the doctors to interpret more data faster and in greater detail, which makes for more accurate decision making. One such model that has been proven effective visualises heart valves for heart surgery. Providing the doctors with video images and structure identification helps make cardiac surgery safer. These cardioscopy tools can be used in other areas which call for less invasive procedures, such as surgery on the brain. This technology has been developed to identify structures, control some surgical procedures, and even educate the surgeon themselves.

Border Delay Prediction

In logistics, one of the most important aspects to consider is the delivery of goods. The unpredictability of border control can sometimes create unpredictable delays, causing disruptions across the entire supply chain. These possibilities need to be accounted for before delivery routes are developed. Machine Learning engines can use previous border control data to make predictions about possible future delays. The models can also work with real time traffic data and border inspection time data to give the logistics company instant information that they can base decisions on. These techniques to predict delays allow the companies to make strategic decisions that they may not have considered otherwise. Models can also ensure that the supply chain is optimised, improving all-around efficiency. Prediction models in logistics have far-reaching effects on the system as a whole.


In the modern world, everyone is connected at all times. We can always find ourselves whenever we get lost thanks to Google maps and sophisticated GPS tools. This constant connection allows us to define well thought out mobility operations across large areas. Artificial Intelligence allows us to optimise mobility systems across cities and between areas. This kind of technological development not only works inside countries and cities, but it can create vast networks of global mobility services. Artificial Intelligence and Machine Learning engines can help to strategically organise transport systems and help companies and countries attract citizens and employees. Some platforms have been developed which help users utilise smart mobility applications such as route planners and public transport schedules. Mobility can play a major role in the choices a person makes as to which city to live in or which job to apply for. With the integration of AI in the mobility field, everyone can be right where they need to be at the right time.

Smart Cities

One application of AI is in the creation of Smart Cities. A Smart City is defined as a city or built-up urban area which utilises technology from the Internet of Things (IoT) devices and manages the city in effective and efficient ways. There are sensors all around the city that collect data from its inhabitants, their devices, transport and traffic. This collected data is then analysed and decisions can be made. Smart City functionalities include the use of automated transport, adaptive energy systems and improving air quality. An AI powered smart city means that the people and the government can directly interact with city processes, either improving them or simply keeping tabs on what’s happening. The major driving forces behind the development of Smart Cities are climate change, the transition to the digital age and pressure from those living in urban areas. Artificial Intelligence will soon become a normal part of city life, and will reduce costs and resource usage, whilst improving living standards across many urban areas.

Store Layout Optimization

The layout of a store is vital to making sales and gaining return shoppers. If the store is messy and unorganised, without a logical flow to it, many customers would choose to shop elsewhere. This is relevant for supermarkets, retail and fashion stores regardless of size. Whenever a person walks into a store to go shopping, data is collected about them. From the path they take to the things they buy, every moment they are in that store, data will be created. Previously, this data was used by people who would make decisions on what products should go where. They would then go to the physical store and make those changes. However, this is a time-consuming action that relies on human decisions. By using data for each shopper, and Artificial Intelligence, data scientists can optimize a shop’s layout, product and banner placement. All of this can be visualized in virtual reality. This saves time and allows designers to create and test different scenarios, in order to determine the most effective layout for the store.

Marketing Campaign Effectiveness

In the modern world people expect a personalised shopping and brand experience. Delivering that can be hard for companies who don’t utilise Artificial Intelligence. Driving marketing with data is the most effective way to ensure your customers are satisfied and feel special. This strategy is fast becoming the normal way marketing campaigns are developed and can actually cost a business less than previous strategies would have.  AI can help in the marketing world by increasing the personalisation of marketing campaigns, analysing consumer patterns, predicting customer purchase probability and much more. Data driven marketing and sales will have a direct impact on both the consumer and the business or brand. This new way of gaining in-depth knowledge of a customer through data and analytical strategies helps increase retention, sales and customer loyalty. It can also identify trends which are likely to happen in the future, meaning that marketing teams can remain ahead of the game at all times.

Demand Forecasting

Demand forecasting is a sales term that is used to explain the method of using past data and sales as indicators for future sales opportunities. Demand forecasting is an essential tool which can provide estimates to companies in order to optimize sales. Data on both successful and unsuccessful sales can allow the team to foresee the goods and services that a client is more likely to need and want. This in turn can lead to higher conversion rates, better customer loyalty and better business decisions. Demand forecasting also decreases the probability of risky decisions stemming from poor planning and understanding. The more data a business has on their typical customer, the better the demand forecasting results. This leads to higher revenues and customer satisfaction. Demand forecasting tools implement Machine Learning models for time-series analysis to predict future demand and inform business decisions.

Automated Customer Service

Generating the perfect customer service experience is key to many businesses. The faster a customer gets a response, the happier they will usually be. Having said that, sometimes call centre operatives don’t have the right answer on hand and need some time to find it before they can provide assistance. This can cause an unpleasant customer experience. By automating customer service with messenger apps or chat bots powered by Natural Language Processing models, the time taken to deliver answers to customers can be significantly reduced. Sometimes a human operator may not know the correct answer to a question a customer asks, leading to varying information across the customer base. A well-defined automated customer service system, however, will be able to provide customers with a reliable 24/7 service that gives consistently correct and up to date answers. Apart from the benefits for the customers, automating customer service will relieve the operatives of mundane tasks that waste time, increasing business revenue.

Anomaly Detection & Predictive Maintenance

Sometimes when a machine is going to break down, there are signs and symptoms. These signs would be invisible until the very last minute to a human engineer, and the machine would break or quality would drop significantly until the problem is solved. This decreases productivity and can be costly to fix in case of a large breakdown. Machine Learning algorithms can be trained to detect anomalies in the data that they receive. By training the machine learning algorithms to classify the normal and aberrant behaviour of machines and processes we are able to bring attention to any characteristics or processes that fall outside of what they should be. This decreases the chances of large breakdowns due to overlooked problems. Applied to predictive maintenance, these models can provide cost optimization to any production environment by defining the optimal maintenance interval for the current state of the equipment. Such information can also inform decisions for preemptive measures in environments where a failure in unacceptable.


Many businesses use targeted marketing where they may use your name in emails that they send to you and recommend products similar to those that you have previously purchased. Although this improves revenue, there is an even more efficient alternative available: hyper-personalization. This new method of targeted marketing is more personalised and can utilise much more data than previous marketing methods can; through trained recommender systems and grouping models. With the ever-evolving capabilities of Artificial Intelligence and Machine Learning, personalising marketing materials is easier than ever. Algorithms pooling data from numerous digital sources are able to increase customer conversions and retention. Hyper-personalization has the ability to give customers exactly what they want when they want while driving business growth.

Worker Safety

Some workplaces are more dangerous than others. Large industrial manufacturing and warehousing complexes are among the most dangerous places to work. The use of Artificial Intelligence to constantly monitor situations and events in a dangerous workplace is currently gaining traction. By using cameras and other real time sensors Artificial Intelligence systems relying on image recognition and dynamic systems representations are able to keep track of complex working environments. This can lower the risk for workplace-associated accidents and workplace dangers. Monitoring everything through sensors and the Internet of Things ensures that employees are wearing the correct safety clothing and using the equipment in a correct and safe way. This kind of technology can significantly reduce work related accidents and essentially improve the workflow in large industries.

Quality Control

Businesses are always looking for new ways to develop an edge over the competition. One area companies are optimising is quality control. As the quality of a product or system is often paramount to the success of the business, it makes sense to incorporate AI solutions that can monitor quality. Another aspect to quality control is the monitoring of equipment. Using technology such as Augmented Reality and Machine Learning to constantly observe and analyse the status of the equipment in use allows companies to understand when something needs to be replaced or fixed. This means that every process in the industry environment is consistently monitored and is less likely to cause harm. The digital twin methodology for recreating industrial environments has evolved in order to provide such constant monitoring. Coupled with advanced modelling of processes, this technology can provide managers of industrial sites with insights that are not available in any other way.

Computer Vision for Retail

Computer vision is a branch of Machine Learning which enables computers to gain meaning from real-time images. The idea behind computer vision is to process images and videos at the same level of competency as a human can. It relies on Machine Learning algorithms like Convolutional Neural Networks, which are designed to evaluate images. Many companies have started incorporating computer vision into the retail industry. Computer vision in retail has been developed as a way for physical stores to remain relevant as online shopping pushes their business into a niche. Computer vision can be used to track the customer in the store and figure out which marketing tactics are working and which are not. It also analyses shopper actions and the ranking of shop departments by popularity. This can help retailers improve the shopping experience by adjusting customer-staff interaction based on the profile of the user. It is also one of the fundamental algorithms behind the Amazon Go store which is run without any employees. Amazon’s model relies on computer vision to detect what the customer places into their basket and takes the payment from their registered credit card automatically.

Voice Integration

Voice integration is the act of controlling a computer with one’s voice. With technologies such as the Amazon Echo and Google Home, a person can search for things, ask questions and get calendar alerts with their voice alone. This kind of technology has been around for years, but, as algorithms have been trained and retrained on more data, its capabilities have improved. Voice integration also has valuable applications for businesses. An example is a version of Alexia which has been developed specifically for business and can help employees make calls and track many aspects of their jobs. Voice-controlled technologies can also encourage users with disabilities to do more shopping online, or increase customer loyalty for online shops that support searching by voice. Voice commands speed up searches and organizational activities and increase value for businesses and consumers alike.

Image Classification

Computers can extract information from images through Computer Vision, but this process is facilitated by classifying parts of the image into a specific class. This is done in AI using Convolutional Neural Networks. Convolutional networks boil images down into broad visual patterns, allowing them to be recognized in other images. The medical industry can use image classification to help doctors identify diseases and even diagnose disease. This kind of application can improve the speed and accuracy of diagnosis. Another potential use for image classification is for security purposes, e.g. face recognition integrated into a security system allows it to distinguish between homeowners and intruders to maintain the security of one’s home.

Text Extraction

Machine Learning models can be taught to detect and read text from an image. This is sometimes referred to as Optical Character Recognition: the process of converting images into computer text. Images can contain typed or handwritten text from a scanned document, a picture, or a frozen video image. These models use pattern recognition algorithms and have multiple use cases. Some applications of text detection models in business include data entry, passport recognition, business card information retrieval, and testing the efficiency of CAPTCHA anti-bot systems. Other functions include helping blind people read documents, using handwriting tools to control computers, and searching online. This technology enables the fast digitalization of printed texts, increasing the availability of documents.

Customer Profiling

Customer profiling is the categorization of customers using the data they generate. After the customer has been categorised the company can make decisions based on their service and what their customers expect. Each grouping of customers will share similar needs and characteristics and lets a company streamline decisions benefitting a wide range of their customer base. The three ways to profile a customer are: through their lifestyle and their consumer decisions; based on the type of products they buy; or the way they buy products. Using AI and data to categorize customers is a powerful approach that helps companies bolster sales and increase customer satisfaction, revenues, and market share.

Fleet Optimisation

Fleet optimisation focuses on the optimisation of a business’ fleet. This may include not only regular vehicles but also things like wind turbines. Optimising a fleet can take many forms, from ensuring the correct routes are chosen, to managing fuel and maintenance costs. The main aim of all optimisation protocols is the same, and that is to reduce costs whilst maintaining the highest possible outcome.

Medical Image Diagnostics

Medical imaging is used to observe what is happening inside the body so that medical practitioners can make decisions about procedures, and understand the patient’s illness better. This kind of technology, such as the X-Ray, has been around for years and is nothing new. However, the entry of Artificial Intelligence into the medical field has allowed the development of new methodologies that “look under the skin”. This improvement has made it easier than ever to reveal internal structures of the human body so that a doctor can make the most accurate diagnosis for their patients. Machine Learning models in the fields of Computer Vision and Image Classification are now able to correctly sift through pools of data and help doctors make decisions in hours that would have otherwise taken weeks.

Predictive maintenance

Predictive maintenance is where software or sensors will monitor the performance of machines whilst they are working. It is different to preventive maintenance which can only perform diagnostic tasks when machines are not working. Predictive maintenance has been a function of machine maintenance for years, and has only recently started to become more digitised through the use of IoT and AI. Although predictive maintenance is not a tool that can be applied to one single structure or machine, it is a complete vision of the machines productivity. This means that consistent monitoring of machines with self-learning models will make sure each machine is working to its highest possible ability. Predictive maintenance is able to read the machine parameters with digital twin technology and put this data into actionable insights that will ensure quality of the machines which will equate to higher revenues, less troubleshooting and more productivity of each machine.

Natural Language Understanding

In a similar vein to automated customer service, natural language understanding can allow a computer model to interact with customers. Instead of using a chat bot or messenger app, natural language understanding will allow a computer actually understand humans in the various ways that they use language. When a person calls customer care, it is highly likely that they would have similar if not the same questions. This is where automated agents would be most effective. Using natural language processing would allow an automated agent to take care of the more mundane tasks which will free up time for the help centre agents to focus on more detailed tasks. This will save time and money for both business and client. A learning algorithm in an automated customer interface will allow the system to handle customer claims in a logical and timely fashion. It will be able to learn the intricacies of the business so that it can truly help and support the customer and employee.